Radiology Gets Chatty: The ChatGPT Saga Unfolds

As artificial intelligence (AI) continues to evolve and mature, it is increasingly finding applications in the field of healthcare, particularly in specialties like radiology that are data-heavy and image-focused. Language learning models (LLMs) such as OpenAI's Generative Pre-trained Transformer-4 (GPT-4) are new in the field of medicine and there is a paucity of literature regarding the possible utilities of GPT-4 given its novelty. We aim to present an in-depth exploration of the role of GPT-4, an advanced language model, in radiology. Giving the GPT-4 model prompts for generating reports, template generation, enhancing clinical decision-making, and suggesting captivating titles for research articles, patient communication, and education, can occasionally be quite generic, and at times, it may present factually incorrect content, which could lead to errors. The responses were then analyzed in detail regarding their potential utility in day-to-day radiologist workflow, patient education, and research processes. Further research is required to evaluate LLMs' accuracy and safety in clinical practice and to develop comprehensive guidelines for their implementation.


Introduction
Radiology is an important component of modern healthcare, providing clinicians with essential information for accurate diagnosis and management of various medical conditions. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to the development of innovative tools that can enhance the diagnostic capabilities of radiologists.
ChatGPT (Chat Generative Pre-training Transformer), an advanced language learning model (LLM) developed by OpenAI (San Francisco, California, United States) [1], is trained on a massive dataset of text and code and can generate text, translate languages, write different kinds of creative content, and answer questions in an informative way [2]. It has shown promising utility for radiologists to improve diagnostic efficiency, increase the accuracy of report translation in various languages, and the breaking down terminology into easier-to-understand language [3]. We aim to discuss the role of ChatGPT in radiology, outlining the applications (with the help of relevant examples of human prompts and AI responses), with a balanced and comprehensive insight into ChatGPT's applications, limitations, and ethical considerations in radiology.

Materials And Methods
This is a cross-sectional technology validation study. The study utilizes real-time prompting and response evaluation from the LLM, GPT-4 (Generative Pre-trained Transformer-4). No real patient data or variables are used in this study. The primary objective was to demonstrate the role of ChatGPT in radiology, outlining the applications with relevant examples of human prompts and AI responses.

Study procedure
For this study, we used OpenAI's GPT-4, the latest version of the model available at the time of writing this article in the first week of May 2023. enhancing clinical decision-making, suggesting captivating titles for research articles, and patient communication and education. One follow-up prompt was used in enhancing clinical decision-making and generating captivating titles for research article sections each. Otherwise, no manipulation or refinement of the prompts was performed during the study.

Validation
The responses were captured as is, without alteration. The responses were then analyzed in detail in regard to their potential utility in day-to-day radiologist workflow, patient education, and use in research processes by an expert radiologist and co-author (HG).

Information Analyses
The information was subsequently analyzed for context and content that is not only applicable to providers but also useful to the patient.

Ethical considerations
Since the study focuses on utilizing real-time indications and evaluating responses from the LLM, GPT-4 AI tool, without using data, biological samples, or patient participation, for purposes such as generating reports, creating templates, enhancing clinical decision-making, suggesting captivating titles for research articles, and patient communication and education, it does not require validation by ethics committees in accordance with the Helsinki Declaration.

Results
Here, we present several prompts and responses from GPT-4 and clinicians' analysis on these. Also, we have summarized them as applications of ChatGPT in Radiology.

Generating reports
ChatGPT can automatically generate simpler or less-complicated radiology reports based on conventional imaging findings by conducting extensive searches of existing literature related to radiology, significantly cutting down on radiologists' report writing time. These automated reports have demonstrated a high degree of accuracy, consistency, and clinical relevance [4]. However, the practicality of these generated reports may be questionable and appears more as an unusual novelty, given that current radiology dictation software utilizes various voice commands to produce pre-stored templates quickly for reporting [5]. An example of human prompting and ChatGPTs response is:

Template generation
Template generation in radiology aims at producing structured reports from free text descriptions in an effort to streamline report production more quickly and effectively. ChatGPT leverages natural language processing (NLP) techniques to extract relevant data and organize it according to predefined templates. This may include patient demographics, clinical history, imaging modality used, anatomical structures examined, or pathologic findings. Radiology reports are generated using free text by extracting information corresponding to sections of the template, populating said template with relevant data, and making sure language and format meet standard radiology reporting guidelines and terminology [6]. Below is an example response generated by Chat GPT after being given instructions to create a CT lung cancer screening protocol.

Patient communication and education
ChatGPT can serve as an invaluable aid for radiologists and care team providers when conveying complex information to patients in an understandable manner. Utilizing NLP capabilities, ChatGPT generates patient-oriented explanations of radiological findings as well as implications for care using tailored prompts incorporated within its NLP model to deconstruct complex medical terminology into an easily understandable narrative [6]. Here is an example of simplification by ChatGPT of an entire radiology report. ChatGPT response: I'm not a doctor, but I can try to help you understand the terminology in your MRI report. It seems to be describing a finding in your liver: "5 cm arterially enhancing lesion": A lesion (abnormal tissue) measuring 5 centimeters is seen in the liver.
It appears to be receiving blood supply from the arteries, which is causing it to stand out on the MRI. "Segment VII of liver": The liver is divided into segments, and this lesion is found in the seventh segment. In summary, your MRI report describes a 5 cm lesion in segment VII of your liver, with characteristics that suggest it is likely to be hepatocellular carcinoma (liver cancer). However, you should consult your doctor to discuss these findings, receive a proper diagnosis, and discuss any necessary next steps."

ChatGPT as a conversational AI assistant for patient queries
Integrating ChatGPT as a chatbot for patient inquiries involves the creation of an engaging, user-friendly platform whereby patients may pose their inquiries and receive accurate, clear, and tailored responses regarding medical imaging results or healthcare concerns. This can be accomplished by setting out the scope, such as radiology reports, medical terminology, follow-up recommendations, and customizing and training ChatGPT using relevant datasets while designing an intuitive user experience and accessibility options. Integration with electronic health records (EHRs) that adhere to privacy regulations can create an enhanced patient experience, with constant monitoring of performance and gathering user feedback for continuous service improvement and producing accurate, up-to-date, and relevant data [7].

Enhance clinical decision-making processes
ChatGPT cannot directly interpret images yet can play an integral part in clinical decision-making by synthesizing meta-analyses, clinical trials, and research papers into summaries that add value to the decision-making process. When provided with details from an imaging modality's findings, the model provides a list of differential diagnoses for consideration.
ChatGPT can serve as an aid to radiologists conducting intelligent research [6]. Radiologists should utilize creativity when drafting prompts for ChatGPT to obtain desired information yet verify and apply one's medical knowledge in order to reduce potential errors [7]. Below is an example of how ChatGPT may assist radiologists by helping generate differentials. Both are benign bone lesions, but they have different characteristics and treatment options.

Propose compelling and captivating titles for research publications
ChatGPT can be an effective tool to generate impactful titles for research articles. By providing essential details such as research questions and topics to its model, ChatGPT can create compelling titles which capture readers' interest while broadening the dissemination of findings. Below is an example of how ChatGPT generates titles for an article.
Human prompt 1: Suggest me some titles for a review article on the role of ChatGPT in radiology.

Generate engaging and impactful headings for scholarly articles
Though ChatGPT cannot entirely produce academic papers on its own, it provides invaluable assistance in organizing the manuscript and suggesting essential components for each section. When given sufficient information about a research question or hypothesis, ChatGPT offers preliminary language that could form part of an introduction section. For authors writing non-native language papers, ChatGPT can assist by editing drafts to make them grammatically correct and clearly articulated [8].

Formatting and references for research papers
Academic radiologists must ensure accurate referencing. ChatGPT can provide invaluable assistance with formatting bibliographies in accordance with various citation styles like American Psychological Association (APA), Modern Language Association (MLA), or the Chicago referencing styleto streamline this process [8].
However, the inability of ChatGPT to generate accurate references is a major limitation. Fabricated and nonexistent references are also noticed while testing its functionality. Clinicians and researchers should be mindful of this significant drawback.

Discussion
Large language learning models such as ChatGPT showed a reasonable output after giving targeted prompts. It could be a useful tool, not only as a virtual assistant [9] but also help with streamlining various tasks for radiologists and other healthcare providers [10][11][12].
It is important to note that the last training data for ChatGPT is from September 2021, at the time of writing this article. Also, while it can provide follow-up recommendations for medical imaging, it is possible that these recommendations may be inaccurate or may not reflect the opinion of a radiologist. As such, the follow-up recommendations generated by ChatGPT should not be entirely relied upon for medical decisionmaking or patient care. Radiologists must exercise their own professional judgment and base their recommendations on the specific clinical context of each patient's case.
The latest version of ChatGPT (GPT-4) is based on an extensive dataset, trained using a significant number of neural networks, which allows it to perform complex analytical tasks [13]. While ChatGPT can generate text-based reports based on input text, its clinical utility remains questionable as it cannot directly interpret medical images.
ChatGPT is indeed effective at generating a comprehensive list of differentials when provided with a description of imaging findings. However, its inability to accept and analyze actual images considerably limits its utility as an assistant for radiologists in their daily work, as of now. Furthermore, ChatGPT can sometimes appear overly agreeable, which may be potentially hazardous for novice trainees who rely on it for guidance. As such, radiologists should always be mindful of these limitations and never solely depend on it for generating differentials.

Limitations and ethical considerations
Despite the numerous benefits provided by ChatGPT in radiology, it is essential to recognize its limitations and address potential ethical concerns. As an AI tool, ChatGPT is prone to errors and biases which are inherent in the training data, potentially leading to inaccuracies. The growing reliance on AI systems raises questions regarding the role of radiologist expertise in decision-making and the implications for accountability and liability. When ChatGPT contributes to clinical decisions, it is vital to ensure that radiologists remain engaged in the process, offering oversight, and applying their medical knowledge and expertise. Establishing clear policies for ChatGPT usage and error management is also essential [8].
Integrating ChatGPT with EHRs presents challenges, primarily concerning patient privacy and potential data leakage, which would violate Health Insurance Portability and Accountability Act (HIPAA) regulations [2]. Additionally, achieving seamless coordination between ChatGPT and EHRs necessitates cooperation among multiple IT platforms, a complex task due to the numerous variables involved.
Furthermore, ChatGPT's training data is limited to information available up until September 2021 (compared to the writing of this article on May 2023) which poses a significant delay as it may not incorporate the latest advancements or updated guidelines in radiology, potentially affecting the accuracy of the generated content.
The information generated by ChatGPT can occasionally be quite generic, and at times, it may present factually incorrect content, which could lead to errors. As such, users should exercise caution when relying on AI-generated content [14]. Interestingly, it has been observed that when the chatbot is repeatedly asked similar questions, the responses can vary, creating potential confusion for users seeking to learn more about a disease process or, even more critically, the prognosis of a specific condition. These limitations present challenges for the direct implementation of chatbots in both trainee and patient education; however, they may still serve as a valuable supplementary tool when used with caution.

Future directions
Currently, ChatGPT, in its fourth iteration, lacks the capability to process image input and therefore cannot assist with image interpretation directly. Further research is required to evaluate LLMs' accuracy and safety in clinical practice and to develop comprehensive guidelines for their implementation. The field of AIpowered chatbots is constantly evolving with daily advancements, such as the recent Bard Experiment by Google (Google LLC, Mountain View, California, United States) [15]. However, determining their appropriate roles and level of autonomy presents challenges. Additionally, ambiguity around the legal liability associated with their use necessitates clear guidelines for their implementation [16].

Conclusions
ChatGPT's role as a virtual assistant with the help of human prompts and AI responses has shown tremendous opportunities in our pilot study for streamlining various tasks for radiologists. Large language learning models such as ChatGPT possess significant potential to function as versatile virtual assistants for other healthcare providers as well. As this technology is still in its early stages, the future role of ChatGPT and AI in radiology remains to be seen. It is crucial to address the limitations and ethical considerations to ensure its safe and responsible integration into clinical practice.

Additional Information Disclosures
Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest:
In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.